● Database Arms Race
Within Five Years, the Operational Database Landscape Will Shift… The Core Turning Point Databricks Sees in the AI Era
Key Takeaways You Need to See Now
Databricks expects the operational database market to undergo a major reshaping within the next five years.
The core point is that the era of moving data elsewhere for analysis is giving way to a structure where real-time data is stored immediately and connected directly to AI.
At the center of this shift is Lakebase, and in Korea, financial institutions are expected to be among the first to respond.
Another important point is that AI competition is no longer just a battle over model performance, but is shifting into a competition over operational systems that include operational databases, governance, real-time processing, and business workflow integration.
In this article, we will organize in a news-style format why Databricks is focusing on operational databases, what Lakebase means, why finance is the core point in the Korean market, and the hidden points that other articles often overlook.
News Summary: Databricks Takes Aim at the Operational Database Market
David Meyer, Senior Vice President of Product at Databricks, said that “the operational database space will change dramatically within five years.”
He emphasized that for companies to use data effectively in the AI era, they need a structure in which real-time data is stored the moment it is generated and then connected to analytics and AI usage.
Databricks plans to target this trend with Lakebase.
Lakebase is a serverless PostgreSQL-based operational database that quickly processes transactional data generated by applications and AI agents while allowing it to be managed under the same governance framework as lakehouse analytics and AI workloads.
Put simply, it is a move toward connecting “service data” and “analytics data” more naturally instead of keeping them separate.
Why Operational Databases Have Become Important Again
Operational databases store and retrieve data whenever it is generated in actual service environments.
Examples include orders, payments, logins, customer status changes, and task results from AI agents.
Speed is the lifeline of this type of database.
For analytical databases, a response within about one second is usually enough, but operational databases require response speeds of around 10 milliseconds.
This difference is not just a performance gap; it is a standard that separates service quality from AI execution speed.
As the number of AI agents increases, the importance of operational databases that can “write immediately and store immediately” will inevitably grow.
Why Databricks Is Moving into Operational Databases
Databricks has been known as a data and AI platform company that supports data storage, analytics, and AI development.
However, the situation changed as AI applications expanded.
Companies have increasingly tried to reduce their dependence on existing SaaS products and build their own AI-based business systems.
In this trend, simply collecting and analyzing data is not enough.
An operational database capable of quickly handling real-time transactions becomes essential.
Databricks is targeting precisely this opening.
Its vision is to apply the success formula of the lakehouse, which reshaped analytical databases, to operational databases as well.
Why Lakebase Is Drawing Attention
Lakebase is not just a simple new product.
It is significant because it breaks down the boundary between operational databases and analytics and AI environments.
Databricks studied the technology for about five years to build Lakebase and began discussing solutions with the Neon team about two years ago.
It later acquired Neon and launched Lakebase in 2025.
Meyer explained that the idea of separating compute and storage in PostgreSQL itself is highly innovative.
He also revealed that Lakebase has already surpassed 100 million dollars in annual recurring revenue.
In other words, this is not an experimental-stage product, but a business that is already receiving real market response.
The Future Direction of the Database Market in the AI Era
Databricks’ view of the future is quite clear.
Just as the lakehouse changed the analytical database industry in the past, operational databases could be reshaped in a similar way within the next five years.
This also means that the entire enterprise IT structure could change.
In the past, operational databases, analytical databases, and AI development environments were separated from one another.
In the future, however, real-time operational data is likely to flow directly into AI training, inference, and automation as a standard structure.
In this structure, the database becomes not just a storage system, but part of the AI execution engine.
Ultimately, the operational database becomes not a backend technology, but a core asset of AI strategy.
Where the Recently Raised Capital Will Be Used
Databricks recently raised an additional 5 billion dollars, bringing its valuation to around 190 billion dollars.
Its annual recurring revenue has also exceeded 7 billion dollars.
At this level, the market sees the company as one of the leading IPO candidates.
Meyer said the funds will be invested in developing products that support AI usage, such as Lakebase, Genie, and Unity AI Gateway.
In other words, the purpose of the fundraising is not simply to increase scale, but to create products that make it easier to bring AI into real business operations.
This also aligns with the broader trend in which cloud, databases, and AI platforms are being reorganized into one integrated bundle.
What Genie and Unity AI Gateway Mean
Genie is a tool that helps ordinary employees use enterprise data through natural language, even if they are not professional data analysts.
Simply put, it is close to an environment where users can work with data by asking questions, even without knowing SQL.
Databricks is also strengthening integration with existing business tools such as Microsoft Excel and Copilot.
This point matters because it serves as a gateway for AI adoption to spread beyond technical teams and across all business users.
Unity AI Gateway can be interpreted in the same context.
Because it helps manage and connect various AI uses inside an enterprise in one place, it has significant meaning in terms of AI governance and operational efficiency.
Why Finance Matters Most in the Korean Market
Meyer expects demand for Lakebase in the Korean market to grow mainly around the financial sector.
The reason is simple.
Finance has the highest demand for real-time capabilities, and transaction data and customer information must be reflected immediately.
AI-based financial services require response speed, security, regulatory compliance, and data governance at the same time.
To satisfy these conditions, the performance and stability of operational databases become extremely important.
Therefore, while demand may also grow in gaming, healthcare and life sciences, pharmaceuticals, media, and entertainment in Korea, financial services are likely to become the true center of gravity.
The Most Important Point Other Articles Often Miss
The most important point is that “the essence of AI competition is moving down into databases.”
Many people think of AI only as a model competition.
However, in actual enterprise environments, data is more difficult than models, and operational structures are more complex than data.
For AI to truly generate revenue, real-time data must be stored accurately, retrieved immediately, and remain stable even when multiple AI agents use it at the same time.
In other words, future competitiveness is likely to depend less on “which model a company uses” and more on “how fast and consistent the data structure connected to that model is.”
From this perspective, Lakebase is not just a database, but a tool for redesigning operating systems in the AI era.
This trend could change IT investment direction across finance, manufacturing, healthcare, commerce, and media.
What Korean Companies Should Check Now
First, companies need to examine whether the existing structure of managing operational databases and analytical databases separately remains the best approach.
Second, AI adoption should not stop at the pilot stage; companies must see whether it can be connected to real-time operational systems.
Third, architecture design must include data governance, security, and regulatory compliance.
Fourth, companies need to create an environment where business users can work with data directly, because this accelerates the spread of AI.
Fifth, high-real-time industries such as finance, gaming, and healthcare should be viewed as already entering the early phase of change.
This shift is not merely an IT upgrade, but a structural change that will determine enterprise competitiveness.
Interpretation from an Economic and AI Trend Perspective
This issue is not just a story about one Databricks product.
From a global economic perspective, it is a signal that AI infrastructure investment is shifting from GPUs for model training toward data operating systems.
In other words, the next phase of growth is more likely to be captured by companies that embed AI deeply into real work than by companies that simply build AI well.
In this process, the markets for operational databases, cloud, data platforms, and AI governance may grow together.
Companies should view this not as an IT cost, but as a productivity investment, while investors should examine where the next center of gravity in the software value chain will be.
In short, databases are no longer quiet infrastructure working in the background; they are becoming a core growth engine of the AI economy.
< Summary >
The operational database market is rising again because of AI.
Databricks is pushing a structure that directly connects real-time data and AI with Lakebase.
In Korea, the financial sector is likely to respond the fastest.
The core point is not the AI model itself, but the operating system that processes data in real time.
Going forward, databases are likely to become the starting point of AI competitiveness.
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*Source: https://zdnet.co.kr/view/?no=20260831100158
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